Traffic Flow Prediction Method Research Articles

Spatial-temporal Graph Attention Networks for Traffic Flow Forecasting

In order to accurately forecast the road section traffic volume, in this study, a spatial-temporal graph attention network model(GALSTM), which is based on graph attention architecture and long and short memory network(LSTM), is proposed to predict the traffic volume of road section. LSTM network is used to extract the temporal correlation of traffic flow data, and graph attention network is used to get adaptive adjacency matrix at each time step to capture the spatial correlation of road network. The proposed GALSTM model and other frequently-used traffic flow prediction methods were validated by using dataset collected by the California highway administration PeMS. Experimental results on two traffic datasets indicate that GALSTM model achieves the best prediction accuracy in all three evaluation metrics of mean absolute errors, mean absolute percentage errors, and root mean squared errors. GALSTM model can be used as an effective method to forecast the traffic volume of road section.

Traffic Flow Prediction Method Based on Deep Learning

Accurate traffic flow forecasts provide an important data basis for traffic management departments. This paper proposes a traffic flow prediction model based on deep learning, which combines Convolutional Neural Network (CNN), Long Short-term Memory (LSTM) and Support Vector Regression (SVR) features: use CNN neural network to mine the spatial characteristics of traffic flow, and then input the time series features captured by LSTM neural network into the SVR model for traffic prediction. The actual traffic flow data of intersections in Mianyang City are selected to verify the CNN-LSTM-SVR hybrid model, and compare it with the CNN model, LSTM model, and SVR model. The results show that the proposed prediction model has higher prediction accuracy.

Prediction of traffic flow with small time granularity at intersection based on probabilistic network

In order to overcome the inaccuracy of current research results of traffic flow prediction, this paper proposes a prediction method for traffic flow with small time granularity at intersection based on probability network. This method takes one minute as time granularity, collects traffic data such as cross-section flow, section traffic flow velocity data, traffic density, road occupancy, section delay and steering ratio by using RFID technology, and analyzes and processes the data. By introducing Bayesian network in probabilistic network and combining K-nearest neighbor method, historical data and predicted traffic flow state are classified to realize the prediction of traffic flow with small time granularity at intersections. The experimental results show that this method has high prediction accuracy and reliability, and is a feasible traffic flow prediction method.

Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems

In recent years, the Intelligent transportations system (ITS) has received considerable attention, due to higher demands for road safety and efficiency in highly interconnected road networks. As an essential part of ITS, traffic prediction can provide support in many aspects, such as road routing, traffic congestion control, etc. To provide a more comprehensive overview of the role of traffic forecasting in ITS systems, we will first introduce the corresponding ITS applications and discuss how traffic forecasting can improve the performance of these applications. Next, we will introduce the general prediction procedure as well as some basic concepts of traffic flow prediction, followed by a description of a general framework for implementing the traffic flow prediction. In this survey, mainly two sorts of prediction methods are focused, statistics-based and machine learning (ML)-based. These two types of approaches are more used in ITS traffic flow predictions these years, and service for different contexts. Generally speaking, the statistics-based models have better model interpretability, but the rigorous model structure limits the adaptability, while ML-based models are more flexible. Accordingly, we will introduce the characteristics of these two types of methods through specific examples of state-of-the-art approaches. Last but not least, some potential and meaningful development directions corresponding to this domain are introduced to do a great favor for future research.

Urban traffic flow online prediction based on multi‐component attention mechanism

Traffic flow prediction is regarded as an important concept used in traffic planning, traffic design, and traffic management. In this study, the authors propose a multi-component attention (MCA) method for traffic flow prediction, which may jointly and adaptively understand components of long-term trends, seasons, and traffic flow residuals that result from multi-dimensional decomposition. According to the highly non-linear nature of traffic flow, the proposed module consists of a one-dimensional convolutional neural network, a bidirectional long short-term memory, and a bidirectional mechanism with an attention mechanism. The former captures local trend characteristics of residuals, while the latter captures trends and seasonal time adjustments. Due to the randomness, irregularity, and periodicity of traffic flow at intersections, target flow prediction is related to various sequences. Through the introduction of the attention mechanism, highly related historical information may be connected for multi-component flow data in the final prediction. Compared to seasonal autoregressive integral moving average model, artificial neural network, and recurrent neural network, the experimental results demonstrated that the proposed MCA model can meet the accuracy and effectiveness of complex non-linear urban traffic flow prediction models.

GeoTraPredict: A machine learning system of web spatio-temporal traffic flow

Traffic flow prediction is an important component for self-driving. Traffic flow is closely related to population distribution, and the traffic flow is not only related to the absolute number of human population but also to their concerns and interests. Accurate spatio-temporal web traffic flow prediction is critical in many applications, such as bandwidth allocation, anomaly detection, congestion control and admission control. Most existing traffic flow prediction methods use models based on time-series analysis and remain inadequate for many real-world applications. Web traffic flow is found to be strongly associated with the spatio-temporal distribution of the population. Increasingly, it is critical to understand and make decisions based on the relationship between population patterns and web traffic flow patterns. It has been proven that different people have different responses to web events. Due to the complexity of spatial data structures and the huge volume of web traffic flow log data, it is difficult to routinely find the relationship between web events and population distributions without an appropriate processing framework. In this paper, we propose an innovative framework named GeoTrafficPredict to support the accurate spatio-temporal prediction of web traffic flow. GeoTrafficPredict provides a machine learning platform to learn the spatio-temporal pattern of traffic flow and use the pattern to predict the trend in both spatial and temporal dimension. Also, GeoTrafficPredict provide data aggregation portal and cloud-based computation function. GeoTrafficPredict deploys a series of computational images in a cloud computing environment, and the implementation on China’s CSTNET illustrates the performance of our platform.

A combined method for short-term traffic flow prediction based on recurrent neural network

The accurate prediction of real-time traffic flow is indispensable to intelligent transport systems. However, the short-term prediction remains a thorny issue, due to the complexity and stochasticity of the traffic flow. To solve the problem, a combined prediction method for short-term traffic flow based on the autoregressive integral moving average (ARIMA) model and long short-term memory (LSTM) neural network was proposed. The method could make short-term predictions of future traffic flow based on historical traffic data. Firstly, the linear regression feature of the traffic data was captured using the rolling regression ARIMA model; then, backpropagation was used to train the LSTM network to capture the non-linear features of the traffic data; and finally, based on the dynamic weighting of sliding window combined the predicted effects of these two techniques. Using MAE, MSE RMSE and MAPE as evaluation indicators, the prediction performance of the combined method proposed was evaluated on three real highway data sets, and compared with the three comparative baselines of ARIMA and LSTM two single methods and equal weight combination. The experimental results show that the dynamic weighted combination model proposed has better prediction effect, which proves the versatility of this method.

SSGRU: A novel hybrid stacked GRU-based traffic volume prediction approach in a road network

As a potential solution to relieve traffic congestions and help build a more safe traffic system, traffic flow prediction methods are given much attention in recent years. In previous studies, it can be found the machine learning (ML)-based methods are widely used in volume predictions of single roads. However, when applied in a more complicated road network, they usually show low efficiency and need to pay higher computing costs. To solve this problem, an innovative ML-based model, named Selected Stacked Gated Recurrent Units model (SSGRU), is proposed in this paper, which is mainly in allusion to road network traffic flow. There are mainly two parts in this model: one is used to do spatial pattern mining based on linear regression coefficients, and the other one includes a stacked gated recurrent unit (SGRU), which is essential for multi-road traffic flow prediction. As the basic unit, a simple tree structure is adopted to approximate the given road network. Particularly, we implemented our model into both suburban and urban traffic contexts, to prove its high adaptability. The whole evaluation process is based on seven different traffic volume data sets recorded at the 15-min interval, chosen from the England Highways. The results show that our model has higher accuracy than others when applied to a multi-road input infrastructure for all road scenarios.

Short-Term Traffic Flow Prediction of Coastal Cities based on Entropy Weight Method

Wang, H. and Yue, P., 2020. Short-term traffic flow prediction of coastal cities based on entropy weight method. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 739–743. Coconut Creek (Florida), ISSN 0749-0208.The traditional short-term traffic flow prediction method has a high prediction error, so a short-term traffic flow prediction method based on entropy weight method is designed. First, identify the lost data, use the weighted average value of historical trend data and measured data to modify, and standardize the original data to collect the short-term traffic flow data of coastal cities. Then, statistical analysis of the original data set, using the minimum maximum standardization method, linear transformation of the original data, and extract the characteristic factors that affect the traffic flow. Finally, based on the correlation of traffic flow at urban intersections, the short-term traffic flow prediction of coastal cities is completed by using entropy weight method. In the experimental part, the prediction errors of the two methods in the period of 7:00-9:00 and 22:00-24:00 are compared. The comparison results show that the prediction errors of the short-term traffic flow prediction method based on entropy weight method in the period of 7:00-9:00 and 22:00-24:00 are small, which proves that the prediction method of this design has practical application significance.

Hybrid Methods for Short-Term Traffic Flow Prediction Based on ARIMA-GARCH Model and Wavelet Neural Network

AbstractAccurate short-term traffic flow prediction is essential for real-time traffic control. A linear hybrid method and a nonlinear hybrid method for short-term traffic flow prediction are propo...

Short-term Traffic Flow Prediction Based on Deep Learning

The accuracy of short-term traffic flow prediction is affected by two factors: one is the accuracy of data collection, and another is model selection. During data collection, aiming at the high cost and low precision of the traditional use of fixed equipment to collect traffic flow data, this paper proposes an algorithm model combining convolutional neural network and support vector regression, and designs an input matrix considering the influence degree of the road segment. The example is proved that the proposed algorithm model is better than the ARIMA and SVR model, and it is an effective traffic flow prediction method.

Research on Short-term Traffic Flow Forecast and Auxiliary Guidance Based on Artificial Intelligence Theory

With the development of social economy, intelligent transportation system (ITS) has been booming. It is mainly to realize the omni-directional, real-time, accurate and efficient guidance and control of transportation in a large range. Traffic flow prediction, especially short-term traffic flow prediction, is the basis of urban traffic control and guidance, so traffic flow prediction system is one of the important subsystems in its. Short-term traffic flow prediction technology belongs to the important research field of intelligent traffic control and vehicle guidance. It can help cities to conduct intelligent traffic guidance and enable users to choose the optimal path. Prediction of road traffic flow state and auxiliary guidance are very important basic theories. Based on the theory of artificial intelligence, this paper analyzes the short-term traffic flow prediction and auxiliary induction methods, points out the limitations of the existing prediction methods, and proposes a short-term traffic flow intelligent prediction method combined with artificial intelligence technology.

Short-Term Traffic Flow Prediction With Wavelet and Multi-Dimensional Taylor Network Model

Accurate prediction of the traffic state has received sustained attention for its ability to provide the anticipatory traffic condition required for people's travel and traffic management. In this paper, we propose a novel short-term traffic flow prediction method based on wavelet transform (WT) and multi-dimensional Taylor network (MTN), which is named as W-MTN. Influenced by the short-term noise disturbance in traffic flow information, the WT is employed to improve prediction accuracy by decomposing the time series of traffic flow. The MTN model, which exploits polynomials to approximate the unknown nonlinear function, makes full use of periodicity and temporal feature without transcendental knowledge and mechanism of the system to be predicted. Our proposed W-MTN model is evaluated on the traffic flow information in a certain area of Shenzhen, China. The experimental results indicate that the proposed W-MTN model offers better prediction performance and temporal correlation, as compared with the corresponding models in the known literature. In addition, the proposed model shows good robustness and generalization ability, when considering data from the different days and locations.

Short‐term traffic flow prediction of road network based on deep learning

Due to the fact that existing traffic flow forecasting methods cannot completely reflect the real time traffic situation of the road network, a new method of short-term traffic flow prediction is proposed based on deep learning in this study. Firstly, in order to improve the efficiency of data processing, a method of road network data compression is proposed based on correlation analysis and CX decomposition. Secondly, the traffic flow data are divided into trend term and random fluctuation term by spectral decomposition method, and the influence of trend term on prediction accuracy is removed. Finally, by combining a deep belief network model and a kernel extreme learning machine classifier as the prediction model, the essential characteristics of the traffic flow data are extracted by using DBN at the bottom of the network, and the extracted results are input into the kernel extreme learning machine to predict the traffic flow. The actual regional road network traffic flow data are tested to verify the effectiveness of the proposed short-time network traffic flow forecasting method. The results show that the proposed method can not only save 90% of the running time but also the average prediction accuracy of each road section can reach 92%.

Truck Traffic Flow Prediction Based on LSTM and GRU Methods With Sampled GPS Data

Given the enormous traffic issues, such as congestion and crashes, resulting from the conflicts between trucks and passenger cars, an accurate and reliable prediction of truck traffic flow is needed to enhance the traffic flow efficiency and safety in the mixed traffic condition. Enabled by emerging sensing technologies, the GPS data become available and will reveal some insights to improve the understanding of truck traffic flow prediction. In the paper, a novel method of truck traffic flow prediction is proposed by using sampled GPS data in the roadway network. The proposed method consists of two phases, which are expansion and prediction. In the data expansion phase, a piece-wise constant coefficient method is designed to minimize errors between the sampled truck flow and the actual truck flow, where the coefficients are determined according to road grades and traffic flow size. In the prediction phase, Long Short Term Memory (LSTM) and Gated Recursive Unit (GRU) neural network methods are first time employed to improve the prediction accuracy. Considering that the sequence of the expansion and prediction could have different prediction performance, approaches using both `previous-prediction', `post-expansion' and `previous-expansion', `post-prediction' were used and the results compared with the survey data from traffic flows. The results demonstrate that LSTM and GRU have a superior performance compared to existing approaches using SRV and ARIMA for truck traffic flow prediction. For the whole prediction period, LSTM has better prediction results than GRU overall with an accuracy which is 4.10% better than that of GRU. Furthermore, the accuracy of the `previous-prediction', `post-expansion' is 8.26% greater than that of the `previous-expansion', `post-prediction'.

Traffic flow combination forecasting method based on improved LSTM and ARIMA

Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model. It compares the traffic data time singularity with the probability value in the dropout module and combines them at unequal time intervals to achieve an accurate prediction of traffic flow data. Then, we design an adaptive traffic flow embedded system that can adapt to Java, Python and other languages and other interfaces. The experimental results demonstrate that the method based on the SDLSTM - ARIMA model has higher accuracy than the similar method using only autoregressive integrated moving average or autoregressive. Our embedded traffic prediction system integrating computer vision, machine learning and cloud has the advantages such as high accuracy, high reliability and low cost. Therefore, it has a wide application prospect.

Traffic flow combination forecasting method based on improved LSTM and ARIMA

Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the recurrent neural networks (RNN) model. It compares the traffic data time singularity with the probability value in the dropout module and combines them at unequal time intervals to achieve an accurate prediction of traffic flow data. Then, we design an adaptive traffic flow embedded system that can adapt to Java, Python and other languages and other interfaces. The experimental results demonstrate that the method based on the SDLSTM-ARIMA model has higher accuracy than similar methods using only autoregressive integrated moving average. Our embedded traffic prediction system integrating computer vision, machine learning and cloud has the advantages such as high accuracy, high reliability and low cost. Therefore, it has a wide application prospects.

Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection.

As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.

A Traffic Flow Prediction Method Based on Road Crossing Vector Coding and a Bidirectional Recursive Neural Network

Aiming at the problems that current predicting models are incapable of extracting the inner rule of the traffic flow sequence in traffic big data, and unable to make full use of the spatio-temporal relationship of the traffic flow to improve the accuracy of prediction, a Bi-directional Regression Neural Network (BRNN) is proposed in this paper, which can fully apply the context information of road intersections both in the past and the future to predict the traffic volume, and further to make up the deficiency that the current models can only predict the next-moment output according to the time series information in the previous moment. Meanwhile, a vectorized code to screen out the intersections related to the predicting point in the road network and to train and predict through inputting the track data of the selected intersections into BRNN, is designed. In addition, the model is testified through the true traffic data in partial area of Shen Zhen. The results indicate that, compared with current traffic predicting models, the model in this paper is capable of providing the necessary evidence for traffic guidance and control due to its excellent performance in extracting the spatio-temporal feature of the traffic flow series, which can enhance the accuracy by 16.298% on average.

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities and complex patterns. Most existing traffic flow prediction methods, lacking abilities of modeling the dynamic spatial-temporal correlations of traffic data, thus cannot yield satisfactory prediction results. In this paper, we propose a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to solve traffic flow forecasting problem. ASTGCN mainly consists of three independent components to respectively model three temporal properties of traffic flows, i.e., recent, daily-periodic and weekly-periodic dependencies. More specifically, each component contains two major parts: 1) the spatial-temporal attention mechanism to effectively capture the dynamic spatialtemporal correlations in traffic data; 2) the spatial-temporal convolution which simultaneously employs graph convolutions to capture the spatial patterns and common standard convolutions to describe the temporal features. The output of the three components are weighted fused to generate the final prediction results. Experiments on two real-world datasets from the Caltrans Performance Measurement System (PeMS) demonstrate that the proposed ASTGCN model outperforms the state-of-the-art baselines.